Facilitating sparsity adaptive feedback in the delay doppler domain in advanced networks

ABSTRACT

Facilitating sparsity adaptive feedback in the delay doppler domain in advanced networks (e.g., 4G, 5G, 6G, and beyond) is provided herein. Operations of a method can comprise determining, by a first device comprising a processor, a channel covariance matrix in a time-frequency domain based on a channel estimation associated with reference signals received from a second device. The method also can comprise decomposing, by the first device, the channel covariance matrix into a group of component matrices. Further, the method can comprise transforming, by the first device, respective matrices of the group of component matrices into respective covariance matrices in a delay doppler domain. The method also can comprise determining, by the first device, channel state information feedback in the delay doppler domain.

TECHNICAL FIELD

This disclosure relates generally to the field of mobile communicationsand, more specifically, to feedback overhead in massive multiple inputmultiple output communications systems in advanced networks.

BACKGROUND

To meet the huge demand for data centric applications, Third GenerationPartnership Project (3GPP) systems and systems that employ one or moreaspects of the specifications of the Fourth Generation (4G) standard forwireless communications will be extended to a Fifth Generation (5G)and/or Sixth Generation (6G) standard for wireless communications.Unique challenges exist to provide levels of service associated withforthcoming 5G, 6G, or other next generation, standards for wirelesscommunication.

BRIEF DESCRIPTION OF THE DRAWINGS

Various non-limiting embodiments are further described with reference tothe accompanying drawings in which:

FIG. 1 illustrates an example, non-limiting, plot of covariance for atime domain;

FIG. 2 illustrates an example, non-limiting, plot of covariance for aDoppler domain in accordance with one or more embodiments describedherein;

FIG. 3 illustrates a chart of the power of a channel in the timefrequency domain;

FIG. 4 illustrates a chart of the power of the same channel of FIG. 3 inthe delay doppler domain in accordance with one or more embodimentsdescribed herein;

FIG. 5 illustrates an example, non-limiting, block diagram of channelestimation and feedback compression in accordance with one or moreembodiments described herein;

FIG. 6 illustrates a flow diagram of an example, non-limiting,computer-implemented method for facilitating sparsity adaptive feedbackin the delay doppler domain in advanced networks in accordance with oneor more embodiments described herein;

FIG. 7 illustrates a flow diagram of an example, non-limiting,computer-implemented method for selecting a codebook based on a sparsityadaptive feedback in the delay doppler domain in advanced networks inaccordance with one or more embodiments described herein;

FIG. 8 illustrates a flow diagram of an example, non-limiting,computer-implemented method for selecting a codebook based on a sparsityadaptive feedback in the delay doppler domain in advanced networks inaccordance with one or more embodiments described herein;

FIG. 9 illustrates an example block diagram of a non-limiting embodimentof a mobile network platform in accordance with various aspectsdescribed herein;

FIG. 10 illustrates an example block diagram of an example mobilehandset operable to engage in a system architecture that facilitateswireless communications according to one or more embodiments describedherein; and

FIG. 11 illustrates an example block diagram of an example computeroperable to engage in a system architecture that facilitates wirelesscommunications according to one or more embodiments described herein.

DETAILED DESCRIPTION

One or more embodiments are now described more fully hereinafter withreference to the accompanying drawings in which example embodiments areshown. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various embodiments. However, the variousembodiments can be practiced without these specific details (and withoutapplying to any particular networked environment or standard).

Massive Multiple Input Multiple Output (MIMO) is a core technology tomeet the improvements in spectral efficiency envisioned for FifthGeneration (5G) networks and other advanced networks. Massive MIMOsystems boast of a large number of antennas at the base stations,serving tens of active users. Accurate channel state information (CSI)can be critical for the operation of massive MIMO systems. In timedivision duplexing (TDD) systems, where the downlink and uplink areoperated in the same spectrum, CSI can be obtained by invoking channelreciprocity. For frequency division duplexing (FDD) systems, where thedownlink and uplink occur at different parts of the frequency spectrum,CSI is generally obtained through limited feedback from a receiverdevice to a transmitter device. Obtaining CSI over the air is costlybecause of the dimensions of the involved channels in massive MIMOsystems. The feedback overhead generally scales with the number ofantennas. This subsequently affects the scalability of the referencesignal design and hinders the practicality of massive MIMO. In cellularnetworks, such as 5G New Radio (NR) and other advanced networks, theMIMO framework can be designed to operate on all frequency bands, bothFDD and TDD. Further, given that FDD bands are still prevalent foroperators in general (sub-6 GHz bands), reducing the overhead in massiveMIMO FDD systems can be beneficial to the success of massive MIMO inpractical communication networks deployments. The dimensionality problemis clear in the design of the feedback scheme in the first generation ofNR. Obtaining accurate channel state information is beneficial for theoperation of massive MIMO systems. For FDD systems, relying on feedbackfrom the user device to the base station device can result in a largeoverhead in the reference signals and the feedback overhead. Forfull-dimensional (FD-MIMO) systems, a CSI reporting procedure can bebased on beamformed reference signals to improve the feedback frameworkfor systems with a larger number of antennas. In 5G NR, in addition tobeamformed CSI-RS, type II CSI can improve the accuracy of the feedback,especially for multi-user MIMO.

However, the procedures discussed above do not mitigate and/or reducethe feedback overhead, which can be provided with the disclosed aspects.As such, the disclosed aspects can enable a more practical massive MIMOimplementation for FDD bands. Further the disclosed aspects can exploitthe invariance and sparsity of the channel for pilot and feedbackoverhead reduction and/or mitigation for massive MIMO systems.

According to an embodiment, provided is a method that can comprisedetermining, by a first device comprising a processor, a channelcovariance matrix in a time-frequency domain based on a channelestimation associated with reference signals received from a seconddevice. The method also can comprise decomposing, by the first device,the channel covariance matrix into a group of component matrices.Further, the method can comprise transforming, by the first device,respective matrices of the group of component matrices into respectivecovariance matrices in a delay doppler domain. The method also cancomprise determining, by the first device, channel state informationfeedback in the delay doppler domain.

According to some implementations, transforming the respective matricescan comprise applying respective symplectic fourier transforms tomatrices in the group of component matrices. Further to theseimplementations, the respective symplectic fourier transforms can relatea delay Doppler matrix to a reciprocal time frequency.

In some implementations, decomposing the channel covariance matrix intocomponent matrices can comprise decomposing the channel covariancematrix based on a structure of an antenna grid at the second device.According to some implementations, decomposing the channel covariancematrix into component matrices can comprise decomposing the channelcovariance matrix in a vertical domain, a horizontal domain, and anuncorrelated domain.

The method can comprise, according to some implementations, selecting,by the first device, points from a group of points on a delay dopplergrid, wherein a point corresponds to a covariance matrix. Further, themethod can comprise selecting, by the first device, a precoding matrixindex from a group of defined matrices based on the points. The methodalso can comprise indicating, by the first device, the precoding matrixindex to the second device based on a feedback code-book basedprocedure. In an example, selecting the points can comprise choosingvalues of a chosen norm of the respective covariance matrices in thedelay doppler domain.

According to some implementations, the method can comprise determining,by the first device, a codebook according to a location of a covariancematrix and an associated value of a norm function in in a delay dopplergrid.

In some implementations, the method can comprise assigning, by the firstdevice, a first feedback budget level to first samples in a delaydoppler grid determined to have first norm levels above a defined normthreshold and a second feedback budget level to second samples in thedelay doppler grid determined to have second norm levels below thedefined norm threshold. Further to these implementations, the method cancomprise conveying, by the first device, the first samples to the seconddevice prior to the second samples as a function of the first feedbackbudget level and the second feedback budget level.

The method can comprise, according to some implementations, sorting, bythe first device, samples in a delay doppler grid according torespective values of norm functions of the samples. Further, the methodcan comprise assigning, by the first device, feedback bits to thesamples based on the respective norm values. In addition, the method cancomprise conveying, by the first device, a first group of samplesselected from the samples based on the feedback bits.

Another embodiment relates to a system that can comprise a processor anda memory that stores executable instructions that, when executed by theprocessor, facilitate performance of operations. The operations cancomprise receiving reference signals from a second device anddetermining a channel covariance matrix in a time-frequency domain basedon a channel estimation associated with the reference signals. Further,the operations can comprise decomposing the channel covariance matrixinto a group of component matrices. The operations also can comprisetransforming respective matrices of the group of component matrices intorespective covariance matrices in a delay doppler domain. The operationsalso can comprise determining channel state information feedback in adelay doppler domain.

In an example, transforming the respective matrices can compriseapplying respective symplectic fourier transforms to matrices in thegroup of component matrices. Further to this example, the respectivesymplectic fourier transforms can convert reciprocal time frequency intoa delay doppler matrix.

According to some implementations, the operations can comprisedecomposing the channel covariance matrix based on a structure of anantenna grid at the second device. In some implementations, theoperations can comprise decomposing the channel covariance matrix in avertical domain, a horizontal domain, and an uncorrelated domain.

The operations can comprise, according to some implementations,selecting points from a group of points on a delay doppler grid, whereina point corresponds to a covariance matrix. Further, the operations cancomprise selecting a precoding matrix index from a group of definedmatrices based on the points. The operations also can compriseindicating the precoding matrix index to the second device based on afeedback code-book based procedure. Further to these implementations,selecting the points comprises choosing values of a chosen norm of therespective covariance matrices in the delay doppler domain.

Another embodiment can relate to a machine-readable storage medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations. The operations can compriseperforming a channel estimation based on reference signals received froma network device. The operations also can comprise determiningcovariance matrices in a time-frequency domain based on the channelestimation. Further, the operations can comprise decomposing thecovariance matrices into a first covariance matrix, a second covariancematrix, and a third covariance matrix. The operations also can compriseobtaining a group of covariance matrices in a delay doppler domain basedon application of respective transforms to the first covariance matrix,the second covariance matrix, and the third covariance matrix.

According to some implementations, the operations can compriseperforming grid subsampling based on the group of covariance matrices inthe delay doppler domain. Further, the operations can comprisefacilitating a transmission to the network device, wherein thetransmission comprises quantized feedback based on the performing thegrid subsampling.

In some implementations, decomposing the covariance matrices into thefirst covariance matrix, the second covariance matrix, and the thirdcovariance matrix can comprise decomposing the covariance matrices intoa vertical domain, a horizontal domain, and an uncorrelated domain.

Accordingly, discussed herein is a feedback compression scheme thatexploits the invariance and sparsity of the channel in the delay Dopplerdomain, such that the limited feedback can be performed based on thedelay doppler transformation of the covariance matrix of the channel,and subsampling the delay-doppler grid to decrease the feedbackoverhead.

Referring initially to FIG. 1, illustrated is an example, non-limiting,plot 100 of covariance for a time domain. Time samples 102 areillustrated along the horizontal axis; covariance norm 104 isillustrated along the vertical axis. A first time sample is illustratedby the first line 106 and a second time sample is illustrated by thesecond line 108. The covariance norm 104 is shown by averaging over thefrequency samples as a function of time samples 102.

By comparison, FIG. 2 illustrates an example, non-limiting, plot 200 ofcovariance for a Doppler domain in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity. Doppler samples 202 are illustrated along the horizontal axis.A first time sample is illustrated by the first line 204 and a secondtime sample is illustrated by the second line 206. The covariance normof the same channel in the delay doppler domain is shown by averagingover the delay samples, as a function of the Doppler samples 202.

As can be determined based on a comparison between FIG. 1 and FIG. 2,for two channel instances at two different times, the channel in timefrequency domain is variant (e.g., the first line 106 and the secondline 108). In contrast, the channel in the delay doppler domain isinvariant to time window change (e.g., the first line 204 and the secondline 206). Leveraging this invariance can be utilized for compressingthe feedback in FDD systems.

An example of the sparsity of the channel in the delay doppler domain isgiven in FIG. 3 and FIG. 4. FIG. 3 illustrates a chart 300 of the powerof a channel in the time frequency domain. Frequency 302 is illustratedalong the X-axis; power 304 is illustrated along the Y-axis; and time306 is illustrated along the Z-axis.

Further, FIG. 4 illustrates a chart 400 of the power of the same channelof FIG. 3 in the delay doppler domain in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity. In this case, delay 402 is illustrated along the X-axis andDoppler 404 is illustrated along the Z-axis. As illustrated, some valuesor points can be high and other points can be low (e.g., at or nearzero). Using the sparsity of the channel in the delay doppler domain canbe utilized, as discussed herein, to compress the feedback in FDD MIMOsystems.

FIG. 5 illustrates an example, non-limiting, block diagram 500 ofchannel estimation and feedback compression in accordance with one ormore embodiments described herein. As illustrated, input signals, suchas reference signals 502 can be received and channel estimation 504 canbe performed. After the channel estimation 504, a covariance matrix 506in the time frequency domain can be computed, and then decomposed intocomponent matrices 508. In an example, this can be performed byfollowing the structure of the antenna grid at the transmitter. In oneexample, this decomposition can be in the vertical, horizontal, anduncorrelated domains, respectively.

A Symplectic Fourier Transform (SFFT) can then be applied to eachcomponent covariance matrix, as indicated at 510. Applying the SFFT canobtain the covariance matrix in the delay doppler domain. The SFFTrelates a N×M delay Doppler matrix x(nΔτ,mΔυ) to a reciprocal M×N timefrequency X(m′Δt,n′Δf) as follows:

${X\left( {{m^{\prime}\Delta \; t},{n^{\prime}\Delta \; f}} \right)} = {\sum\limits_{n = 0}^{N - 1}{\sum\limits_{m = 0}^{M - 1}{{\exp \left( {j2{\pi \left( {\frac{m^{\prime}m}{M} - \frac{n^{\prime}n}{N}} \right)}} \right)}{x\left( {{n\Delta \tau},\ {m\Delta v}} \right)}}}}$

The SFFT transform can be equivalent to an application of N-dimensionalFFT along the columns of x(nΔτ,mΔυ), followed by an inverseM-dimensional FFT transform along its rows.

According to an embodiment, after obtaining the covariance matrix in thedelay-doppler domain, at 512, a dual stage framework can be implemented.In the dual stage framework, L points can be selected on the delaydoppler grid out of the N×M possible points. For each of these selectedL points, where every point corresponds to a covariance matrix, alimited feedback codebook-based scheme can be utilized to indicate theprecoding matrix index (PMI) to the transmitter device (e.g., the basestation device). Note that the preferred precoder might not beexplicitly indicated to the network, but its index (PMI) can be chosenfrom a set of predefined matrices forming a codebook fed back to thenetwork (e.g., the transmitter device), at 514, where the same codebookof these predefined matrices is available at both the receiver device(e.g., the UE device) and the transmitter device (e.g., the network).

The selection of L points out of N×M points can be performed accordingto the power spectrum of the covariance matrices in the delay dopplerdomain. Given the sparsity of the covariance matrix representation inthe delay doppler domain, L out of N×M selected points whose power isabove a certain threshold can be selected for feedback. The L selectedpoints and their relative position in the delay doppler grid can be fedback such that at the base station (e.g., the transmitter device), thedelay doppler grid can be reconstructed from the set of observed Lpoints, given the sparsity of the grid. The L samples are picked suchthat the grid can be reconstructed at the transmitter (e.g., the networkdevice). An example of the reconstruction framework can be based oncompressed sampling. Compressed sampling considers the sparsereconstruction problem of estimating an unknown sparse vector x from anobserved vector of measurements y. The reconstruction can be subject tothe constraint that x is S sparse (e.g., at most S of its entries arenon zero). The positions (indices) on the non zero entries of x areunknown.

In another embodiment, the subsampling of the grid in the delay dopplerdomain can be performed such that to ensure backward compatibility,given that existing orthogonal frequency division multiple access(OFDMA) systems, such as the 3GPP 5G NR system, have predefinedsubcarrier spacings and symbol durations. The delay doppler grid can besubsampled to control the overhead of the CSI feedback. The subsamplingcan be regular or irregular. In case of regular subsampling, values foreach point of the grid can be reported including those with zero energy.Zero energy here means that the energy is below a predefined orconfigurable threshold. Alternatively, in case of irregular subsampling,only non-zero values are reported, in which case only the values above acertain threshold are reported.

In another embodiment, the feedback compression can be performedjointly, such that the codebook that the PMI is based on, is chosenaccording to the location of the covariance matrix and its amplitudevalue (e.g. power) in the delay doppler grid. Samples in the delaydoppler grid whose power are above a certain threshold, can be given ahigher feedback budget (correspondingly a larger codebook which the PMIis based on). Other samples below a certain threshold can be given alower feedback budget.

In another embodiment, the total feedback budget is a predeterminedvalue. The samples in delay doppler grid are sorted according to theamplitude value. The feedback bits of each sample can be determined bythe size of codebook for PMI. Only the top N samples are fed back suchthat the total number of feedback bits are within the budget. Theremainder of the samples are not fed back (not included in the feedbackbudget)

In another embodiment, the total number of samples in feedback is apre-determined value. Then the samples in delay doppler grid are sortedaccording to the amplitude value and only the first N values areincluded in the feedback.

As discussed herein, the disclosed aspects can exploit the invarianceand sparsity of the channel in the delay doppler domain. Further, thedisclosed aspects can allow a receiver device (e.g., a UE device) tohave a high flexibility in choosing the CSI feedback and reducing theCSI overhead. In addition, the disclosed aspects can make closed loopMIMO more realistic in high velocity UE devices (>300 km/h) because ofthe invariance of the channel in the delay doppler domain, and theability to feed back CSI less frequently. Additionally, the disclosedaspects can make massive MIMO deployment in FDD frequencies morefeasible.

For example, in massive MIMO, the number of antennas is increased andthe feedback overhead and pilot, such as reference signal designoverhead can also increase due to the need to feedback information aboutmore channels between the base station and the UE device. In accordancewith the disclosed aspects, the overhead can be reduced and, atsubstantially the same time, the performance can be improved.

As discussed, a time-frequency grid is converted to a new grid, referredto as a delay doppler grid. In the delay doppler grid, the channel ismore invariance and much more sparse as compared to the channel in thetime-frequency grid, as discussed with respect to FIGS. 1-4 above.

Methods that can be implemented in accordance with the disclosed subjectmatter, will be better appreciated with reference to various flowcharts. While, for purposes of simplicity of explanation, the methodsare shown and described as a series of blocks, it is to be understoodand appreciated that the disclosed aspects are not limited by the numberor order of blocks, as some blocks can occur in different orders and/orat substantially the same time with other blocks from what is depictedand described herein. Moreover, not all illustrated blocks can berequired to implement the disclosed methods. It is to be appreciatedthat the functionality associated with the blocks can be implemented bysoftware, hardware, a combination thereof, or any other suitable means(e.g., device, system, process, component, and so forth). Additionally,it should be further appreciated that the disclosed methods are capableof being stored on an article of manufacture to facilitate transportingand transferring such methods to various devices. Those skilled in theart will understand and appreciate that the methods could alternativelybe represented as a series of interrelated states or events, such as ina state diagram.

FIG. 6 illustrates a flow diagram of an example, non-limiting,computer-implemented method 600 for facilitating sparsity adaptivefeedback in the delay doppler domain in advanced networks in accordancewith one or more embodiments described herein.

In some implementations, a system comprising a processor can perform thecomputer-implemented method 600 and/or other methods discussed herein.In other implementations, a device comprising a processor can performthe computer-implemented method 600 and/or other methods discussedherein. In other implementations, a machine-readable storage medium, cancomprise executable instructions that, when executed by a processor,facilitate performance of operations, which can be the operationsdiscussed with respect to the computer-implemented method 600 and/orother methods discussed herein. In further implementations, a computerreadable storage device comprising executable instructions that, inresponse to execution, cause a system comprising a processor to performoperations, which can be operations discussed with respect to thecomputer-implemented method 600 and/or other methods discussed herein.

At 602 of the computer-implemented method 600, a first device candetermine a channel covariance matrix in a time-frequency domain basedon a channel estimation associated with reference signals received froma second device. For example, the first device can be a UE device andthe second device can be a base station device.

The channel covariance matrix can be decomposed into a group ofcomponent matrices, at 604 of the computer-implemented method 600. Forexample, decomposing the channel covariance matrix into componentmatrices can comprise decomposing the channel covariance matrix in avertical domain, a horizontal domain, and an uncorrelated domain.

Further, at 606 of the computer-implemented method 600, the first devicecan transform respective matrices of the group of component matricesinto respective covariance matrices in a delay doppler domain. In anexample, transforming the respective matrices can comprise applyingrespective symplectic fourier transforms to matrices in the group ofcomponent matrices. The respective symplectic fourier transforms canrelate a delay Doppler matrix to a reciprocal time frequency.Accordingly, at 608, the first device can determine channel stateinformation feedback in the delay doppler domain.

FIG. 7 illustrates a flow diagram of an example, non-limiting,computer-implemented method 700 for selecting a codebook based on asparsity adaptive feedback in the delay doppler domain in advancednetworks in accordance with one or more embodiments described herein.

In some implementations, a system comprising a processor can perform thecomputer-implemented method 700 and/or other methods discussed herein.In other implementations, a device comprising a processor can performthe computer-implemented method 700 and/or other methods discussedherein. In other implementations, a machine-readable storage medium, cancomprise executable instructions that, when executed by a processor,facilitate performance of operations, which can be the operationsdiscussed with respect to the computer-implemented method 700 and/orother methods discussed herein. In further implementations, a computerreadable storage device comprising executable instructions that, inresponse to execution, cause a system comprising a processor to performoperations, which can be operations discussed with respect to thecomputer-implemented method 700 and/or other methods discussed herein.

Upon or after converting matrices of a group of component matrices (of atime-frequency domain) into respective covariance matrices in a delaydoppler domain, as discussed with respect to FIG. 6, at 702 of thecomputer-implemented method 700, a first device can select points from agroup of points on a delay doppler grid. A point can correspond to acovariance matrix. For example, selecting the points can comprisechoosing values of a chosen norm of the respective covariance matricesin the delay doppler domain. The values chosen can be the valuesdetermined to be the best values of the chosen norm, according to someimplementations.

A precoding matrix index can be selected by the first device, at 704.For example, the precoding matrix index can be selected from a group ofdefined matrices based on the points. Further, at 706 of thecomputer-implemented method 700, the first device can indicate theprecoding matrix index to the second device based on a feedbackcode-book based procedure.

FIG. 8 illustrates a flow diagram of an example, non-limiting,computer-implemented method 800 for selecting a codebook based on asparsity adaptive feedback in the delay doppler domain in advancednetworks in accordance with one or more embodiments described herein.

In some implementations, a system comprising a processor can perform thecomputer-implemented method 800 and/or other methods discussed herein.In other implementations, a device comprising a processor can performthe computer-implemented method 800 and/or other methods discussedherein. In other implementations, a machine-readable storage medium, cancomprise executable instructions that, when executed by a processor,facilitate performance of operations, which can be the operationsdiscussed with respect to the computer-implemented method 800 and/orother methods discussed herein. In further implementations, a computerreadable storage device comprising executable instructions that, inresponse to execution, cause a system comprising a processor to performoperations, which can be operations discussed with respect to thecomputer-implemented method 800 and/or other methods discussed herein.

At 802 of the computer-implemented method 800, a receiver devicecomprising a processor can perform a channel estimation based onreference signals received from a network device.

Further, at 804, the receiver device can determine covariance matricesin a time-frequency domain based on the channel estimation. Thecovariance matrices can be decomposed, by the receiver device, into afirst covariance matrix, a second covariance matrix, and a thirdcovariance matrix, at 806. For example, decomposing the covariancematrices into the first covariance matrix, the second covariance matrix,and the third covariance matrix can comprise decomposing the covariancematrices into a vertical domain, a horizontal domain, and anuncorrelated domain.

A group of covariance matrices in a delay doppler domain can be obtainedat 808 of the computer-implemented method 800. For example, obtaining agroup of covariance matrices can be based on application of respectivetransforms to the first covariance matrix, the second covariance matrix,and the third covariance matrix.

Further, at 810, a grid subsampling can be performed based on the groupof covariance matrices in the delay doppler domain. A transmission to atransmitter device (e.g., a network device) can be facilitated by thereceiver device at 812. The transmission can comprise quantized feedbackbased on the performing the grid subsampling.

In an example, facilitating the transmission can comprise determining acodebook according to a location of a covariance matrix and anassociated value of a norm function in a delay doppler grid.

In another example, a first feedback budget level can be assigned tofirst samples in a delay doppler grid determined to have first powerlevels above a defined power threshold and a second feedback budgetlevel to second samples in the delay doppler grid determined to havesecond power levels below the defined power threshold. The first samplescan be conveyed to the transmitter device (e.g., the network device)prior to the second samples as a function of the first feedback budgetlevel and the second feedback budget level.

According to some implementations, samples in a delay doppler grid canbe stored according to respective amplitude values of the samples.Further, feedback bits can be assigned to the samples based on therespective amplitude values. A first group of samples selected from thesamples can be conveyed to the transmitter device based on the feedbackbits.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate sparsityadaptive feedback in the delay doppler domain in advanced networks.Facilitating sparsity adaptive feedback in the delay doppler domain canbe implemented in connection with any type of device with a connectionto the communications network (e.g., a mobile handset, a computer, ahandheld device, etc.) any Internet of things (IoT) device (e.g.,toaster, coffee maker, blinds, music players, speakers, etc.), and/orany connected vehicles (e.g., cars, airplanes, boats, space rockets,and/or other at least partially automated vehicles (e.g., drones), andso on). In some embodiments, the non-limiting term User Equipment (UE)is used. It can refer to any type of wireless device that communicateswith a radio network node in a cellular or mobile communication system.Examples of UE are target device, device to device (D2D) UE, machinetype UE or UE capable of machine to machine (M2M) communication, PDA,Tablet, mobile terminals, smart phone, Laptop Embedded Equipped (LEE),laptop mounted equipment (LME), USB dongles etc. Note that the termselement, elements and antenna ports can be interchangeably used butcarry the same meaning in this disclosure. The embodiments areapplicable to single carrier as well as to Multi-Carrier (MC) or CarrierAggregation (CA) operation of the UE. The term Carrier Aggregation (CA)is also called (e.g., interchangeably called) “multi-carrier system,”“multi-cell operation,” “multi-carrier operation,” “multi-carrier”transmission and/or reception.

In some embodiments, the non-limiting term radio network node or simplynetwork node is used. It can refer to any type of network node thatserves one or more UEs and/or that is coupled to other network nodes ornetwork elements or any radio node from where the one or more UEsreceive a signal. Examples of radio network nodes are Node B, BaseStation (BS), Multi-Standard Radio (MSR) node such as MSR BS, eNode B,network controller, Radio Network Controller (RNC), Base StationController (BSC), relay, donor node controlling relay, Base TransceiverStation (BTS), Access Point (AP), transmission points, transmissionnodes, Remote Radio Unit (RRU), a Remote Radio Head (RRH), nodes inDistributed Antenna System (DAS) etc.

The various aspects described herein can relate to New Radio (NR), whichcan be deployed as a standalone radio access technology or as anon-standalone radio access technology assisted by another radio accesstechnology, such as Long Term Evolution (LTE), for example.

It should be noted that although various aspects and embodiments havebeen described herein in the context of 5G, 6G, Universal MobileTelecommunications System (UMTS), and/or Long Term Evolution (LTE), orother next generation networks, the disclosed aspects are not limited to5G, 6G, a UMTS implementation, and/or an LTE implementation as thedisclosed aspects can also be applied in 3G, 4G, 5G, 6G, or LTE systems.For example, aspects or features of the disclosed embodiments can beexploited in substantially any wireless communication technology. Suchwireless communication technologies can include, but are not limited to,UMTS, Code Division Multiple Access (CDMA), Wireless Fidelity (Wi-Fi),Worldwide Interoperability for Microwave Access (WiMAX), General PacketRadio Service (GPRS), Enhanced GPRS, Third Generation PartnershipProject (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2)Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), EvolvedHigh Speed Packet Access (HSPA+), High-Speed Downlink Packet Access(HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or anotherIEEE 802.XX technology. Additionally, substantially all aspectsdisclosed herein can be exploited in legacy telecommunicationtechnologies.

As used herein, “5G” can also be referred to as NR access. Accordingly,systems, methods, and/or machine-readable storage media for facilitatingfacilitate sparsity adaptive feedback in the delay doppler domain inadvanced networks are desired. As used herein, one or more aspects of a6G network can comprise, but is not limited to, data rates of severaltens of megabits per second (Mbps) supported for tens of thousands ofusers; at least one gigabit per second (Gbps) to be offeredsimultaneously to tens of users (e.g., tens of workers on the sameoffice floor); several hundreds of thousands of simultaneous connectionssupported for massive sensor deployments; spectral efficiencysignificantly enhanced compared to 4G; improvement in coverage relativeto 4G; signaling efficiency enhanced compared to 4G; and/or latencysignificantly reduced compared to LTE.

In addition, advanced networks, such as a 6G network can be configuredto provide more bandwidth than the bandwidth available in other networks(e.g., 4G network, 5G network). A 6G network can be configured toprovide more ubiquitous connectivity. In addition, more potential ofapplications and services, such as connected infrastructure, wearablecomputers, autonomous driving, seamless virtual and augmented reality,“ultra-high-fidelity” virtual reality, and so on, can be provided with6G networks. Such applications and/or services can consume a largeamount of bandwidth. For example, some applications and/or services canconsume about fifty times the bandwidth of a high-definition videostream, Internet of Everything (IoE), and others. Further, variousapplications can have different network performance requirements (e.g.,latency requirements and so on).

Cloud Radio Access Networks (cRAN) can enable the implementation ofconcepts such as SDN and Network Function Virtualization (NFV) in 6Gnetworks. This disclosure can facilitate a generic channel stateinformation framework design for a 6G network. Certain embodiments ofthis disclosure can comprise an SDN controller that can control routingof traffic within the network and between the network and trafficdestinations. The SDN controller can be merged with the 6G networkarchitecture to enable service deliveries via open ApplicationProgramming Interfaces (APIs) and move the network core towards an allInternet Protocol (IP), cloud based, and software driventelecommunications network. The SDN controller can work with, or takethe place of, Policy and Charging Rules Function (PCRF) network elementsso that policies such as quality of service and traffic management androuting can be synchronized and managed end to end.

FIG. 9 presents an example embodiment 900 of a mobile network platform910 that can implement and exploit one or more aspects of the disclosedsubject matter described herein. Generally, wireless network platform910 can include components, e.g., nodes, gateways, interfaces, servers,or disparate platforms, that facilitate both packet-switched (PS) (e.g.,Internet protocol (IP), frame relay, asynchronous transfer mode (ATM)and circuit-switched (CS) traffic (e.g., voice and data), as well ascontrol generation for networked wireless telecommunication. As anon-limiting example, wireless network platform 910 can be included intelecommunications carrier networks, and can be considered carrier-sidecomponents as discussed elsewhere herein. Mobile network platform 910includes CS gateway node(s) 912 which can interface CS traffic receivedfrom legacy networks such as telephony network(s) 940 (e.g., publicswitched telephone network (PSTN), or public land mobile network (PLMN))or a signaling system #7 (SS7) network 960. Circuit switched gatewaynode(s) 912 can authorize and authenticate traffic (e.g., voice) arisingfrom such networks. Additionally, CS gateway node(s) 912 can accessmobility, or roaming, data generated through SS7 network 960; forinstance, mobility data stored in a visited location register (VLR),which can reside in memory 930. Moreover, CS gateway node(s) 912interfaces CS-based traffic and signaling and PS gateway node(s) 918. Asan example, in a 3GPP UMTS network, CS gateway node(s) 912 can berealized at least in part in gateway GPRS support node(s) (GGSN). Itshould be appreciated that functionality and specific operation of CSgateway node(s) 912, PS gateway node(s) 918, and serving node(s) 916, isprovided and dictated by radio technology(ies) utilized by mobilenetwork platform 910 for telecommunication. Mobile network platform 910can also include the MMEs, HSS/PCRFs, SGWs, and PGWs disclosed herein.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 918 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions caninclude traffic, or content(s), exchanged with networks external to thewireless network platform 910, like wide area network(s) (WANs) 950,enterprise network(s) 970, and service network(s) 980, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 910 through PS gateway node(s) 918. It is to benoted that WANs 950 and enterprise network(s) 970 can embody, at leastin part, a service network(s) such as IP multimedia subsystem (IMS).Based on radio technology layer(s) available in technology resource(s)917, packet-switched gateway node(s) 918 can generate packet dataprotocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 918 can includea tunnel interface (e.g., tunnel termination gateway (TTG) in 3GPP UMTSnetwork(s) (not shown)) which can facilitate packetized communicationwith disparate wireless network(s), such as Wi-Fi networks.

In embodiment 900, wireless network platform 910 also includes servingnode(s) 916 that, based upon available radio technology layer(s) withintechnology resource(s) 917, convey the various packetized flows of datastreams received through PS gateway node(s) 918. It is to be noted thatfor technology resource(s) 917 that rely primarily on CS communication,server node(s) can deliver traffic without reliance on PS gatewaynode(s) 918; for example, server node(s) can embody at least in part amobile switching center. As an example, in a 3GPP UMTS network, servingnode(s) 916 can be embodied in serving GPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)914 in wireless network platform 910 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format, and so on) such flows. Suchapplication(s) can include add-on features to standard services (forexample, provisioning, billing, user support, and so forth) provided bywireless network platform 910. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 918 for authorization/authentication and initiation of a datasession, and to serving node(s) 916 for communication thereafter. Inaddition to application server, server(s) 914 can include utilityserver(s), a utility server can include a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through wireless network platform 910 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 912and PS gateway node(s) 918 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 950 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to wirelessnetwork platform 910 (e.g., deployed and operated by the same serviceprovider), such as femto-cell network(s) (not shown) that enhancewireless service coverage within indoor confined spaces and offload RANresources in order to enhance subscriber service experience within ahome or business environment by way of UE 975.

It is to be noted that server(s) 914 can include one or more processorsconfigured to confer at least in part the functionality of macro networkplatform 910. To that end, the one or more processor can execute codeinstructions stored in memory 930, for example. It should be appreciatedthat server(s) 914 can include a content manager 915, which operates insubstantially the same manner as described hereinbefore.

In example embodiment 900, memory 930 can store information related tooperation of wireless network platform 910. Other operationalinformation can include provisioning information of mobile devicesserved through wireless network platform network 910, subscriberdatabases; application intelligence, pricing schemes, e.g., promotionalrates, flat-rate programs, couponing campaigns; technicalspecification(s) consistent with telecommunication protocols foroperation of disparate radio, or wireless, technology layers; and soforth. Memory 930 can also store information from at least one oftelephony network(s) 940, WAN 950, enterprise network(s) 970, or SS7network 960. In an aspect, memory 930 can be, for example, accessed aspart of a data store component or as a remotely connected memory store.

Referring now to FIG. 10, illustrated is an example block diagram of anexample mobile handset 1000 operable to engage in a system architecturethat facilitates wireless communications according to one or moreembodiments described herein. Although a mobile handset is illustratedherein, it will be understood that other devices can be a mobile device,and that the mobile handset is merely illustrated to provide context forthe embodiments of the various embodiments described herein. Thefollowing discussion is intended to provide a brief, general descriptionof an example of a suitable environment in which the various embodimentscan be implemented. While the description includes a general context ofcomputer-executable instructions embodied on a machine-readable storagemedium, those skilled in the art will recognize that the innovation alsocan be implemented in combination with other program modules and/or as acombination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules, orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM,digital video disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information, and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The handset includes a processor 1002 for controlling and processing allonboard operations and functions. A memory 1004 interfaces to theprocessor 1002 for storage of data and one or more applications 1006(e.g., a video player software, user feedback component software, etc.).Other applications can include voice recognition of predetermined voicecommands that facilitate initiation of the user feedback signals. Theapplications 1006 can be stored in the memory 1004 and/or in a firmware1008, and executed by the processor 1002 from either or both the memory1004 or/and the firmware 1008. The firmware 1008 can also store startupcode for execution in initializing the handset 1000. A communicationscomponent 1010 interfaces to the processor 1002 to facilitatewired/wireless communication with external systems, e.g., cellularnetworks, VoIP networks, and so on. Here, the communications component1010 can also include a suitable cellular transceiver 1011 (e.g., a GSMtransceiver) and/or an unlicensed transceiver 1013 (e.g., Wi-Fi, WiMax)for corresponding signal communications. The handset 1000 can be adevice such as a cellular telephone, a PDA with mobile communicationscapabilities, and messaging-centric devices. The communicationscomponent 1010 also facilitates communications reception fromterrestrial radio networks (e.g., broadcast), digital satellite radionetworks, and Internet-based radio services networks.

The handset 1000 includes a display 1012 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 1012 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 1012 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface1014 is provided in communication with the processor 1002 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1394) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This can support updating andtroubleshooting the handset 1000, for example. Audio capabilities areprovided with an audio I/O component 1016, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 1016 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 1000 can include a slot interface 1018 for accommodating aSIC (Subscriber Identity Component) in the form factor of a cardSubscriber Identity Module (SIM) or universal SIM 1020, and interfacingthe SIM card 1020 with the processor 1002. However, it is to beappreciated that the SIM card 1020 can be manufactured into the handset1000, and updated by downloading data and software.

The handset 1000 can process IP data traffic through the communicationscomponent 1010 to accommodate IP traffic from an IP network such as, forexample, the Internet, a corporate intranet, a home network, a personarea network, etc., through an ISP or broadband cable provider. Thus,VoIP traffic can be utilized by the handset 1000 and IP-based multimediacontent can be received in either an encoded or decoded format.

A video processing component 1022 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 1022can aid in facilitating the generation, editing, and sharing of videoquotes. The handset 1000 also includes a power source 1024 in the formof batteries and/or an AC power subsystem, which power source 1024 caninterface to an external power system or charging equipment (not shown)by a power I/O component 1026.

The handset 1000 can also include a video component 1030 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 1030 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 1032 facilitates geographically locating the handset 1000. Asdescribed hereinabove, this can occur when the user initiates thefeedback signal automatically or manually. A user input component 1034facilitates the user initiating the quality feedback signal. The userinput component 1034 can also facilitate the generation, editing andsharing of video quotes. The user input component 1034 can include suchconventional input device technologies such as a keypad, keyboard,mouse, stylus pen, and/or touchscreen, for example.

Referring again to the applications 1006, a hysteresis component 1036facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 1038 can be provided that facilitatestriggering of the hysteresis component 1036 when the Wi-Fi transceiver1013 detects the beacon of the access point. A SIP client 1040 enablesthe handset 1000 to support SIP protocols and register the subscriberwith the SIP registrar server. The applications 1006 can also include aclient 1042 that provides at least the capability of discovery, play andstore of multimedia content, for example, music.

The handset 1000, as indicated above related to the communicationscomponent 1010, includes an indoor network radio transceiver 1013 (e.g.,Wi-Fi transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode GSM handset 1000. The handset 1000 canaccommodate at least satellite radio services through a handset that cancombine wireless voice and digital radio chipsets into a single handhelddevice.

Referring now to FIG. 11, illustrated is an example block diagram of anexample computer 1100 operable to engage in a system architecture thatfacilitates wireless communications according to one or more embodimentsdescribed herein. The computer 1100 can provide networking andcommunication capabilities between a wired or wireless communicationnetwork and a server (e.g., Microsoft server) and/or communicationdevice. In order to provide additional context for various aspectsthereof, FIG. 11 and the following discussion are intended to provide abrief, general description of a suitable computing environment in whichthe various aspects of the innovation can be implemented to facilitatethe establishment of a transaction between an entity and a third party.While the description above is in the general context ofcomputer-executable instructions that can run on one or more computers,those skilled in the art will recognize that the innovation also can beimplemented in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the disclosed methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the innovation can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media can embody computer-readable instructions, datastructures, program modules or other structured or unstructured data ina data signal such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference to FIG. 11, implementing various aspects described hereinwith regards to the end-user device can include a computer 1100, thecomputer 1100 including a processing unit 1104, a system memory 1106 anda system bus 1108. The system bus 1108 couples system componentsincluding, but not limited to, the system memory 1106 to the processingunit 1104. The processing unit 1104 can be any of various commerciallyavailable processors. Dual microprocessors and other multi-processorarchitectures can also be employed as the processing unit 1104.

The system bus 1108 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1106includes read-only memory (ROM) 1127 and random-access memory (RAM)1112. A basic input/output system (BIOS) is stored in a non-volatilememory 1127 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1100, such as during start-up. The RAM 1112 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1100 further includes an internal hard disk drive (HDD)1114 (e.g., EIDE, SATA), which internal hard disk drive 1114 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1116, (e.g., to read from or write to aremovable diskette 1118) and an optical disk drive 1120, (e.g., readinga CD-ROM disk 1122 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1114, magnetic diskdrive 1116 and optical disk drive 1120 can be connected to the systembus 1108 by a hard disk drive interface 1124, a magnetic disk driveinterface 1126 and an optical drive interface 1128, respectively. Theinterface 1124 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and IEEE 1394 interfacetechnologies. Other external drive connection technologies are withincontemplation of the subject innovation.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1100 the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer 1100, such aszip drives, magnetic cassettes, flash memory cards, cartridges, and thelike, can also be used in the exemplary operating environment, andfurther, that any such media can contain computer-executableinstructions for performing the methods of the disclosed innovation.

A number of program modules can be stored in the drives and RAM 1112,including an operating system 1130, one or more application programs1132, other program modules 1134 and program data 1136. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1112. It is to be appreciated that the innovation canbe implemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 1100 throughone or more wired/wireless input devices, e.g., a keyboard 1138 and apointing device, such as a mouse 1140. Other input devices (not shown)can include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touchscreen, or the like. These and other input devicesare often connected to the processing unit 1104 through an input deviceinterface 1142 that is coupled to the system bus 1108, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 1144 or other type of display device is also connected to thesystem bus 1108 through an interface, such as a video adapter 1146. Inaddition to the monitor 1144, a computer 1100 typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1100 can operate in a networked environment using logicalconnections by wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1148. The remotecomputer(s) 1148 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentdevice, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer,although, for purposes of brevity, only a memory/storage device 1150 isillustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 1152 and/or larger networks,e.g., a wide area network (WAN) 1154. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which canconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1100 isconnected to the local network 1152 through a wired and/or wirelesscommunication network interface or adapter 1156. The adapter 1156 canfacilitate wired or wireless communication to the LAN 1152, which canalso include a wireless access point disposed thereon for communicatingwith the wireless adapter 1156.

When used in a WAN networking environment, the computer 1100 can includea modem 1158, or is connected to a communications server on the WAN1154, or has other means for establishing communications over the WAN1154, such as by way of the Internet. The modem 1158, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1108 through the input device interface 1142. In a networkedenvironment, program modules depicted relative to the computer, orportions thereof, can be stored in the remote memory/storage device1150. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,and so forth), and telephone. This includes at least Wi-Fi andBluetooth™ wireless technologies. Thus, the communication can be apredefined structure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, in a hotel room, or a conference room at work, withoutwires. Wi-Fi is a wireless technology similar to that used in a cellphone that enables such devices, e.g., computers, to send and receivedata indoors and out; anywhere within the range of a base station. Wi-Finetworks use radio technologies called IEEE 802.11 (a, b, g, etc.) toprovide secure, reliable, fast wireless connectivity. A Wi-Fi networkcan be used to connect computers to each other, to the Internet, and towired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networksoperate in the unlicensed 2.4 and 6 GHz radio bands, at an 9 Mbps(802.11a) or 64 Mbps (802.11b) data rate, for example, or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 16BaseT wired Ethernetnetworks used in many offices.

An aspect of 6G, which differentiates from previous 4G systems, is theuse of NR. NR architecture can be designed to support multipledeployment cases for independent configuration of resources used forRACH procedures. Since the NR can provide additional services than thoseprovided by LTE, efficiencies can be generated by leveraging the prosand cons of LTE and NR to facilitate the interplay between LTE and NR,as discussed herein.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics can be combined in any suitable manner in one or moreembodiments.

As used in this disclosure, in some embodiments, the terms “component,”“system,” “interface,” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution, and/or firmware. As anexample, a component can be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, computer-executable instructions, a program, and/or acomputer. By way of illustration and not limitation, both an applicationrunning on a server and the server can be a component.

One or more components can reside within a process and/or thread ofexecution and a component can be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components can communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software application orfirmware application executed by one or more processors, wherein theprocessor can be internal or external to the apparatus and can executeat least a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confer(s) at least in part the functionalityof the electronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system. While various components have been illustrated asseparate components, it will be appreciated that multiple components canbe implemented as a single component, or a single component can beimplemented as multiple components, without departing from exampleembodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or.” That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “mobile device equipment,” “mobile station,”“mobile,” subscriber station,” “access terminal,” “terminal,” “handset,”“communication device,” “mobile device” (and/or terms representingsimilar terminology) can refer to a wireless device utilized by asubscriber or mobile device of a wireless communication service toreceive or convey data, control, voice, video, sound, gaming orsubstantially any data-stream or signaling-stream. The foregoing termsare utilized interchangeably herein and with reference to the relateddrawings. Likewise, the terms “access point (AP),” “Base Station (BS),”BS transceiver, BS device, cell site, cell site device, “Node B (NB),”“evolved Node B (eNode B),” “home Node B (HNB)” and the like, areutilized interchangeably in the application, and refer to a wirelessnetwork component or appliance that transmits and/or receives data,control, voice, video, sound, gaming or substantially any data-stream orsignaling-stream from one or more subscriber stations. Data andsignaling streams can be packetized or frame-based flows.

Furthermore, the terms “device,” “communication device,” “mobiledevice,” “subscriber,” “customer entity,” “consumer,” “customer entity,”“entity” and the like are employed interchangeably throughout, unlesscontext warrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based on complex mathematical formalisms), which canprovide simulated vision, sound recognition and so forth.

Systems, methods and/or machine-readable storage media for a grouphybrid automatic repeat request procedure for sidelink group-case inadvanced networks are provided herein. Legacy wireless systems such asLTE, Long-Term Evolution Advanced (LTE-A), High Speed Packet Access(HSPA) etc. use fixed modulation format for downlink control channels.Fixed modulation format implies that the downlink control channel formatis always encoded with a single type of modulation (e.g., quadraturephase shift keying (QPSK)) and has a fixed code rate. Moreover, theforward error correction (FEC) encoder uses a single, fixed mother coderate of ⅓ with rate matching. This design does not take into the accountchannel statistics. For example, if the channel from the BS device tothe mobile device is very good, the control channel cannot use thisinformation to adjust the modulation, code rate, thereby unnecessarilyallocating power on the control channel Similarly, if the channel fromthe BS to the mobile device is poor, then there is a probability thatthe mobile device might not able to decode the information received withonly the fixed modulation and code rate. As used herein, the term“infer” or “inference” refers generally to the process of reasoningabout, or inferring states of, the system, environment, user, and/orintent from a set of observations as captured via events and/or data.Captured data and events can include user data, device data, environmentdata, data from sensors, sensor data, application data, implicit data,explicit data, etc. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates of interest based on a consideration of data and events, forexample.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationprocedures and/or systems (e.g., support vector machines, neuralnetworks, expert systems, Bayesian belief networks, fuzzy logic, anddata fusion engines) can be employed in connection with performingautomatic and/or inferred action in connection with the disclosedsubject matter.

In addition, the various embodiments can be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, machine-readable device, computer-readablecarrier, computer-readable media, machine-readable media,computer-readable (or machine-readable) storage/communication media. Forexample, computer-readable media can comprise, but are not limited to, amagnetic storage device, e.g., hard disk; floppy disk; magneticstrip(s); an optical disk (e.g., compact disk (CD), a digital video disc(DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g.,card, stick, key drive); and/or a virtual device that emulates a storagedevice and/or any of the above computer-readable media. Of course, thoseskilled in the art will recognize many modifications can be made to thisconfiguration without departing from the scope or spirit of the variousembodiments

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the subject matter has been described herein inconnection with various embodiments and corresponding figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

1. A method, comprising: determining, by a first device comprising aprocessor, a channel covariance matrix in a time-frequency domain basedon a channel estimation associated with reference signals received froma second device; decomposing, by the first device, the channelcovariance matrix into a group of component matrices; transforming, bythe first device, respective matrices of the group of component matricesinto respective covariance matrices in a delay doppler domain; anddetermining, by the first device, channel state information feedback inthe delay doppler domain.
 2. The method of claim 1, wherein thetransforming the respective matrices comprises applying respectivesymplectic fourier transforms to matrices in the group of componentmatrices.
 3. The method of claim 2, wherein the respective symplecticfourier transforms relate a delay Doppler matrix to a reciprocal timefrequency.
 4. The method of claim 1, wherein the decomposing the channelcovariance matrix into component matrices comprises decomposing thechannel covariance matrix based on a structure of an antenna grid at thesecond device.
 5. The method of claim 1, wherein the decomposing thechannel covariance matrix into component matrices comprises decomposingthe channel covariance matrix in a vertical domain, a horizontal domain,and an uncorrelated domain.
 6. The method of claim 1, furthercomprising: selecting, by the first device, points from a group ofpoints on a delay doppler grid, wherein a point corresponds to acovariance matrix; selecting, by the first device, a precoding matrixindex from a group of defined matrices based on the points; andindicating, by the first device, the precoding matrix index to thesecond device based on a feedback code-book based procedure.
 7. Themethod of claim 6, wherein the selecting the points is performedcomprises choosing values of a chosen norm of the respective covariancematrices in the delay doppler domain.
 8. The method of claim 1, furthercomprising: determining, by the first device, a codebook according to alocation of a covariance matrix and an associated value of a normfunction in a delay doppler grid.
 9. The method of claim 1, furthercomprising: assigning, by the first device, a first feedback budgetlevel to first samples in a delay doppler grid determined to have firstnorm levels above a defined norm threshold and a second feedback budgetlevel to second samples in the delay doppler grid determined to havesecond norm levels below the defined norm threshold; and conveying, bythe first device, the first samples to the second device prior to thesecond samples as a function of the first feedback budget level and thesecond feedback budget level.
 10. The method of claim 1, furthercomprising: sorting, by the first device, samples in a delay dopplergrid according to respective values of norm functions of the samples;assigning, by the first device, feedback bits to the samples based onthe respective norm values; and conveying, by the first device, a firstgroup of samples selected from the samples based on the feedback bits.11. A first device, comprising: a processor; and a memory that storesexecutable instructions that, when executed by the processor, facilitateperformance of operations, comprising: receiving reference signals froma second device; determining a channel covariance matrix in atime-frequency domain based on a channel estimation associated with thereference signals; decomposing the channel covariance matrix into agroup of component matrices; transforming respective matrices of thegroup of component matrices into respective covariance matrices in adelay doppler domain; and determining channel state information feedbackin a delay doppler domain.
 12. The first device of claim 11, wherein thetransforming the respective matrices comprises applying respectivesymplectic fourier transforms to matrices in the group of componentmatrices.
 13. The first device of claim 12, wherein the respectivesymplectic fourier transforms convert reciprocal time frequency into adelay doppler matrix.
 14. The first device of claim 11, wherein theoperations further comprise decomposing the channel covariance matrixbased on a structure of an antenna grid at the second device.
 15. Thefirst device of claim 11, wherein the operations further comprisedecomposing the channel covariance matrix in a vertical domain, ahorizontal domain, and an uncorrelated domain.
 16. The first device ofclaim 11, wherein the operations further comprise: selecting points froma group of points on a delay doppler grid, wherein a point correspondsto a covariance matrix; selecting a precoding matrix index from a groupof defined matrices based on the points; and indicating the precodingmatrix index to the second device based on a feedback code-book basedprocedure.
 17. The first device of claim 16, wherein the selecting thepoints comprises choosing values of a chosen norm of the respectivecovariance matrices in the delay doppler domain.
 18. A non-transitorymachine-readable medium, comprising executable instructions that, whenexecuted by a processor, facilitate performance of operations,comprising: performing a channel estimation based on reference signalsreceived from a network device; determining covariance matrices in atime-frequency domain based on the channel estimation; decomposing thecovariance matrices into a first covariance matrix, a second covariancematrix, and a third covariance matrix; and obtaining a group ofcovariance matrices in a delay doppler domain based on application ofrespective transforms to the first covariance matrix, the secondcovariance matrix, and the third covariance matrix.
 19. Thenon-transitory machine-readable medium of claim 18, wherein theoperations further comprise: performing grid subsampling based on thegroup of covariance matrices in the delay doppler domain; andfacilitating a transmission to the network device, wherein thetransmission comprises quantized feedback based on the performing thegrid subsampling.
 20. The non-transitory machine-readable medium ofclaim 18, wherein the decomposing the covariance matrices into the firstcovariance matrix, the second covariance matrix, and the thirdcovariance matrix comprises decomposing the covariance matrices into avertical domain, a horizontal domain, and an uncorrelated domain.